def findBestPermutationListOPTIM(X1, X2, atomlist, boxl=None):
"""
use OPTIM's minperm() routine to calculate the optimum permutation
"""
#deal with periodic boundary conditions
periodic = boxl is not None
if not periodic:
#it must have a value for passing to fortran
boxl = 1.
sx = sy = sz = boxl
X1 = X1.reshape([-1,3])
X2 = X2.reshape([-1,3])
atomlist = np.array(atomlist)
X13 = X1[atomlist,:].reshape(-1)
X23 = X2[atomlist,:].reshape(-1)
#run the minperm algorithm
perm, dist, worstdist, worstradius = minperm.minperm(X13, X23, sx, sy, sz, periodic)
perm -= 1 #fortran indexing
#note, dist returned by minperm comes will only be accurate to 6 decimal places at best.
#if we want a more accurate distance we should calculate it from the coordinates
# apply the permutation
newperm = np.array(atomlist[perm])
X2new = np.copy(X2)
X2new[atomlist,:] = X2[newperm,:]
dist = np.sqrt(dist)
return dist, X1.reshape(-1), X2new.reshape(-1)
def find_permutations_OPTIM(X1, X2, box_lengths=None, make_cost_matrix=None):
"""
use OPTIM's minperm() routine to calculate the optimum permutation
"""
if make_cost_matrix is not _make_cost_matrix and make_cost_matrix is not None:
raise RuntimeError(
"cannot use a custom cost matrix with findBestPermutationListOPTIM"
)
# deal with periodic boundary conditions
periodic = box_lengths is not None
if not periodic:
# it must have a value for passing to fortran
box_lengths = [1., 1., 1.]
sx, sy, sz = box_lengths
# run the minperm algorithm
perm, dist, worstdist, worstradius = minperm.minperm(
X1.ravel(), X2.ravel(), sx, sy, sz, periodic)
perm -= 1 # fortran indexing
# note, dist returned by minperm comes will only be accurate to 6 decimal places at best.
# if we want a more accurate distance we should calculate it from the coordinates
dist = np.sqrt(dist)
return dist, perm
def findBestPermutationListOPTIM(X1, X2, atomlist, boxl=None):
"""
use OPTIM's minperm() routine to calculate the optimum permutation
"""
#deal with periodic boundary conditions
periodic = boxl is not None
if not periodic:
#it must have a value for passing to fortran
boxl = 1.
sx = sy = sz = boxl
X1 = X1.reshape([-1,3])
X2 = X2.reshape([-1,3])
atomlist = np.array(atomlist)
X13 = X1[atomlist,:].reshape(-1)
X23 = X2[atomlist,:].reshape(-1)
#run the minperm algorithm
perm, dist, worstdist, worstradius = minperm.minperm(X13, X23, sx, sy, sz, periodic)
perm -= 1 #fortran indexing
#note, dist returned by minperm comes will only be accurate to 6 decimal places at best.
#if we want a more accurate distance we should calculate it from the coordinates
# apply the permutation
newperm = np.array(atomlist[perm])
X2new = np.copy(X2)
X2new[atomlist,:] = X2[newperm,:]
dist = np.sqrt(dist)
return dist, X1.reshape(-1), X2new.reshape(-1)
def find_permutations_OPTIM(X1, X2, box_lengths=None, make_cost_matrix=None):
"""
use OPTIM's minperm() routine to calculate the optimum permutation
"""
#print "Using OPTIM minperm"
if make_cost_matrix is not _make_cost_matrix and make_cost_matrix is not None:
raise RuntimeError("cannot use a custom cost matrix with findBestPermutationListOPTIM")
# deal with periodic boundary conditions
periodic = box_lengths is not None
if not periodic:
# it must have a value for passing to fortran
box_lengths = [1., 1., 1.]
sx, sy, sz = box_lengths
# print "passing in periodic:", periodic
# print "box lengths", sx, sy, sz
# run the minperm algorithm
#print "Starting minperm call"
perm, dist, worstdist, worstradius = minperm.minperm(X1.ravel(), X2.ravel(), sx, sy, sz, periodic)
#print "Returned from minperm"
perm -= 1 # fortran indexing
# note, dist returned by minperm comes will only be accurate to 6 decimal places at best.
# if we want a more accurate distance we should calculate it from the coordinates
dist = np.sqrt(dist)
return dist, perm
